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Blogs

FPGAs and TPUs: Why AI Inference Might Need More Than GPUs

GPUs dominate AI, but for inference, specialized hardware like FPGAs and TPUs can offer significant advantages in latency, cost, and power efficiency for specific workloads.

Making Distributed Systems Consistent with Raft

Building reliable distributed systems means handling failures while keeping data consistent. Raft consensus helps manage this complexity through a clear approach to agreement.

Keeping Edge AI Models Fresh with OTA Updates

Edge AI models aren't static. Learn why Over-the-Air updates are critical for maintaining their performance and how they differ from typical software updates.

Multi-Tenant LLMs: Isolation and Resource Management

Serving large language models to multiple users efficiently introduces significant challenges around isolation, performance, and resource management. This article explores practical strategies to handle these complexities.

Time-Series Databases: Beyond Basic Metrics

Time-series databases are purpose-built for the massive amounts of time-stamped data generated by modern systems, offering unique advantages for observability and analytics.

Your AI System Will Fail. Test it with Chaos Engineering

AI systems are complex and fragile. Traditional testing often misses critical failure modes. Chaos Engineering helps you find them before users do.

Data Mesh for AI: Decentralizing Data Governance

Centralized data platforms often bottleneck AI development. Data Mesh offers a decentralized approach, empowering domain teams to own and govern their AI data products.

Canary Deployments: Staged AI Model Rollouts

Deploying new AI models can be risky. Canary deployments offer a safer, staged approach to roll out models in production, minimizing impact from unexpected issues.

Reducing Cold Start Latency for Serverless LLMs

Serverless functions are great for scaling, but LLM inference can hit painful cold starts. Let's look at how to reduce that latency.